In energy economy forecasts of different time series are rudimentary. In this study, a prediction for the German day-ahead spot market is created with Apache Spark and R. It is just an example for many different applications in virtual power plant environments. Other examples of use as intraday price processes, load processes of machines or electric vehicles, real time energy loads of photovoltaic systems and many more time series need to be analysed and predicted. This work gives a short introduction into the project where this study is settled. It describes the time series methods that are used in energy industry for forecasts shortly. As programming technique Apache Spark, which is a strong cluster computing technology, is utilised. Today, single time series can be predicted. The focus of this work is on developing a method to parallel forecasting, to process multiple time series simultaneously with R and Apache Spark.

中文翻译：

---

Apache Spark的时间序列分析及其在能源信息学中的应用

在能源经济中，对不同时间序列的预测是基本的。在这项研究中，使用Apache Spark和R创建了对德国日头现货市场的预测。这只是虚拟电厂环境中许多不同应用程序的示例。还需要分析和预测用作日内价格过程，机器或电动车辆的负荷过程，光伏系统的实时能源负荷以及更多时间序列的其他示例。这项工作简要介绍了完成本研究的项目。它描述了能源行业中用于预测的时间序列方法。作为强大的集群计算技术，Apache Spark被用作编程技术。今天，可以预测单个时间序列。